Note: Descriptions are shown in the official language in which they were submitted.
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DYNAMIC AND PREDICTIVE ADJUSTMENT OF PAYMENT ATTRIBUTES BASED
ON CONTEXTUAL DATA AND METADATA
CROSS-REFERENCE TO RELATED APPLICATIONS
This application claims the benefit of U.S. Patent Application No. 17/488,136,
filed 28
September 2021, titled "DYNAMIC AND PREDICTIVE ADJUSTMENT OF PAYMENT
ATTRIBUTES BASED ON CONTEXTUAL DATA AND METADATA," which claims
priority to U.S. Provisional Patent Application No. 63/084,565, filed 29
September 2020, titled
"MACHINE LEARNING & RISK BA SED AUTOMATON FOR FLEET, FUEL, FUNDING, &
CAR CONTROLS," to U.S. Provisional Patent Application No. 63/152,353, filed 23
February
2021, titled "FLEET CARD AUTHORIZATION UNDER CONDITIONS OF LIMITED
CONNECTIVITY," and to Greek Patent Application No. 20210100451, filed July 1,
2021, titled
-HIGH AVAILABILITY REAL-TIME AUTHORIZATION OF TRANSACTIONS." All of the
above applications are incorporated by reference herein in their entirety for
all purposes.
TECHNICAL FIELD
[001] Embodiments relate generally to predicted adjustment of payment
attributes based on
contextual data, and specifically, to the dynamic and predictive adjustment of
payment attributes
for fuel related transactions.
BACKGROUND
[002] Payment instruments are commonly utilized by businesses for covering
costs incurred
by their employees. For example, in a typical fleet operation, drivers are
provided with credit
cards to make payments for fuel and other miscellaneous expenses that are
incurred during a trip.
[003] However, employees may misuse the payment instruments and utilize
them for
personal expenses. Such misuse may be difficult to detect using conventional
technologies and
techniques.
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SUMMARY
[004] A system of one or more computers can be configured to
perform particular
operations or actions by virtue of having software, firmware, hardware, or a
combination of
them installed on the system that in operation causes or cause the system to
perform the actions.
One or more computer programs can be configured to perform particular
operations or actions
by virtue of including instructions that, when executed by data processing
apparatus, cause the
apparatus to perform the actions. One general aspect includes a method to
update a payment
attribute of a payment instrument associated with a vehicle of a fleet that
includes a plurality of
vehicles. The method also includes receiving, at a computing device, route
data for a plurality of
trips, where the route data for each trip of the plurality of trips includes a
corresponding
geofence region, a corresponding user identifier, a corresponding payment
instrument identifier,
and a corresponding vehicle identifier for each trip; receiving, from a user
device, a
transmission of one or more data packets generated at a software application
executing on the
user device, where the one or more data packets may include a user identifier
and a vehicle
identifier; authenticating, by the computing device, the vehicle identifier
and the user identifier;
identifying a first trip of the plurality of trips based on a match of the
user identifier and the
vehicle identifier with the route data, where the first trip is associated
with a first payment
instrument; receiving, at the computing device, first trip data for the first
trip from the user
device, where the first trip data includes at least location data of the user
device; calculating,
based on the received first trip data, a fuel likelihood score for the first
trip, where the fuel
likelihood score is indicative of a degree of validity of a fuel transaction;
comparing the fuel
likelihood score to a first threshold; based on a determination that the fuel
likelihood score
meets the first threshold: transmitting, to a computer associated with a
payment network, a
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message to update one or more payment attributes stored at the payment network
and associated
with the first payment instrument; and displaying or causing a display, on the
application
executing on the user device, an indication that the first payment instrument
is approved for the
fuel transaction. The method also includes based on a determination that the
fuel likelihood
score does not meet the first threshold and meets a second threshold:
displaying or causing a
display, on the application executing on the user device, a request for
additional verification
data. The method also includes receiving, from the user device, the additional
verification data;
calculating, based on the additional verification data and the received first
trip data, an updated
fuel likelihood score for the first trip; comparing the updated fuel
likelihood score to the first
threshold. The method also includes based on a determination that the updated
fuel likelihood
score meets the first threshold: transmitting to a computer associated with a
payment network, a
message to update one or more payment attributes stored at the payment network
and associated
with the first payment instrument; and displaying or causing a display, on the
application
executing on the user device, an indication that the first payment instrument
is approved for the
fuel transaction. Other embodiments of this aspect include corresponding
computer systems,
apparatus, and computer programs recorded on one or more computer storage
devices, each
configured to perform the actions of the methods.
[005] Implementations may include one or more of the following
features. The method
may include: receiving at two or more groups of authorization control pods, an
authorization
request from the payment network for a fuel transaction, where the
authorization request
includes an identifier associated with the first payment instrument and
transaction information
about the transaction, and where each authorization control pod includes a
processing service, a
database service, and a policy agent; analyzing, at a corresponding policy
agent of a single
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authorization control pod of the groups of authorization control pods, the
transaction
information by comparing one or more transaction attributes in the transaction
information
combined with contextual and historic data against one or more policies stored
in the database
service; and based on the comparison, transmitting one of an approval or a
rejection of the
transaction to the payment network. Calculating the fuel likelihood score for
the first trip may
include providing the first trip data to a trained machine learning model. The
fuel likelihood
score is calculated based on the first trip data and the received telemetric
data; and storing, at
the computing device, the first trip data and the received telemetric data in
a linked data
structure. Receiving the first trip data may include receiving a plurality of
packets transmitted
from the software application at a first periodicity that is based on one or
more of: a time
elapsed since an estimated time of commencement of the trip and a distance
elapsed since the
commencement of the trip. Implementations of the described techniques may
include hardware,
a method or process, or computer software on a computer-accessible medium.
[006] One general aspect includes a method to update a payment
attribute of a payment
instrument. The method also includes receiving, by a computing device may
include a processor
and memory, an indication of a trip being undertaken by a vehicle; associating
with the trip, by
the computing device, a user device, a user identifier, and a vehicle
identifier; receiving, by the
computer device, from an application executing on a user device of a user of
the vehicle, trip
data that includes at least location data obtained from the user device;
calculating, based on the
received trip data, a fuel likelihood score for the trip, where the fuel
likelihood score is
indicative of a degree of validity of a fuel transaction; comparing the fuel
likelihood score to a
first threshold; and based on a determination that the fuel likelihood score
meets the first
threshold: transmitting, to a computer associated with a payment network, a
message to update
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one or more payment attributes that are stored in a device of the payment
network and where the
one or more payment attributes are associated with a payment instrument; and
displaying, on
the application executing on the user device, an indication that the payment
instrument is
approved for the fuel transaction. Other embodiments of this aspect include
corresponding
computer systems, apparatus, and computer programs recorded on one or more
computer
storage devices, each configured to perform the actions of the methods.
[007] Implementations may include one or more of the following
features. The method
may include based on a determination that the fuel likelihood score does not
meet the first
threshold and meets a second threshold: displaying or causing a display, on
the application
executing on the user device, a request for additional verification data;
receiving, from the user
device, the additional verification data; calculating, based on the additional
verification data and
the received trip data, an updated fuel likelihood score for the trip;
comparing the updated fuel
likelihood score to the first threshold; and based on a determination that the
fuel likelihood
score meets the first threshold: transmitting, to a computer associated with a
payment network, a
message to update one or more payment attributes that are stored in a device
of the payment
network and where the one or more payment attributes are associated with the
payment
instrument; and displaying or causing a display, on the application executing
on the user device,
an indication that the payment instrument is approved for the fuel
transaction. Displaying or
causing a display of the request for additional verification data may include
displaying or
causing the display of a request for an image of one or more of: an odometer,
a dashboard, or a
license plate of the vehicle, and where receiving the additional verification
data may include
receiving image data and corresponding image metadata. Calculating the fuel
likelihood score
may include analyzing the received image data and the received image metadata
to determine
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one or more of an odometer mileage and an estimated fuel level. Analyzing the
received image
data and the received image metadata may include determining a distance
traveled since a
previous fueling event. The telemetric data may include two or more of:
location data, fuel level
data, and fuel efficiency data. Calculating the fuel likelihood score may
include applying a
trained machine learning (ml) model to the trip data. The authorization
request includes an
identifier associated with the payment instrument and transaction information
about the
transaction, and where each authorization control pod includes a processing
service, a database
service, and a policy agent; analyzing, at a corresponding policy agent of a
single authorization
control pod of the groups of authorization control pods, the transaction
information by
comparing one or more transaction attributes in the transaction information
combined with
contextual and historic data against one or more policies stored in the
database service; and
based on the comparison, transmitting one of an approval or a rejection of the
transaction to the
payment network. The transaction attributes in the transaction information
received from the
payment network includes a pump location identifier, and where analyzing the
transaction
information may include comparing the pump location identifier to the location
data received
from the user device. Receiving the trip data may include receiving a
plurality of packets
transmitted from the application at a first periodicity that is based on one
or more of: a time
elapsed since an estimated time of commencement of the trip and a distance
elapsed since the
commencement of the trip. Implementations of the described techniques may
include hardware,
a method or process, or computer software on a computer-accessible medium.
[008] One general aspect includes a non-transitory computer-
readable medium may
include instructions that includes receiving, by a computing device may
include a processor and
memory, an indication of a trip being undertaken by a vehicle; associating
with the trip, by the
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computing device, a user device, a user identifier, and a vehicle identifier;
receiving, by the
computer device, from an application executing on a user device of a user of
the vehicle, trip
data that includes at least location data obtained from the user device;
calculating, based on the
received trip data, a fuel likelihood score for the first trip, where the fuel
likelihood score is
indicative of a degree of validity of a fuel transaction; comparing the fuel
likelihood score to a
first threshold; and based on a determination that the fuel likelihood score
meets the first
threshold: transmitting, to a computer associated with a payment network, a
message to update
one or more payment attributes that are stored in a device of the payment
network and where the
one or more payment attributes are associated with a payment instrument; and
displaying, on
the application executing on the user device, an indication that the payment
instrument is
approved for the fuel transaction. Other embodiments of this aspect include
corresponding
computer systems, apparatus, and computer programs recorded on one or more
computer
storage devices, each configured to perform the actions of the methods.
[009] Implementations may include one or more of the following
features. The non-
transitory computer-readable medium where the operations further may include:
based on a
determination that the fuel likelihood score does not meet the first threshold
and meets a second
threshold: displaying or causing a display, on the application executing on
the user device, a
request for additional verification data; receiving, from the user device, the
additional
verification data; calculating, based on the additional verification data and
the received trip data,
an updated fuel likelihood score for the trip; comparing the updated fuel
likelihood score to the
first threshold; and based on a determination that the fuel likelihood score
meets the first
threshold: transmitting, to a computer associated with a payment network, a
message to update
one or more payment attributes that are stored in a device of the payment
network and where the
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one or more payment attributes are associated with the payment instrument; and
displaying or
causing a display, on the application executing on the user device, an
indication that the
payment instrument is approved for the fuel transaction. Displaying or causing
a display of the
request for additional verification data may include displaying or causing the
display of a
request for an image of one or more of: an odometer, a dashboard, or a license
plate of the
vehicle, and where receiving the additional verification data may include
receiving image data
and corresponding image metadata. The operations further may include:
receiving at two or
more groups of authorization control pods, an authorization request from the
payment network
for the fuel transaction, where the authorization request includes an
identifier associated with
the payment instrument and transaction information about the transaction, and
where each
authorization control pod includes a processing service, a database service,
and a policy agent;
analyzing, at a corresponding policy agent of a single authorization control
pod of the groups of
authorization control pods, the transaction information by comparing one or
more transaction
attributes in the transaction information combined with contextual and
historic data against one
or more policies stored in the database service; and based on the comparison,
transmitting one
of an approval or a rejection of the transaction to the payment network.
Implementations of the
described techniques may include hardware, a method or process, or computer
software on a
computer-accessible medium.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 is a diagram of an example system environment for dynamic and
predictive
adjustment of payment attributes based on contextual data and metadata, in
accordance with some
implementations.
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[0011] FIG. 2 depicts an example payment instrument management system, in
accordance with
some implementations.
[0012] FIGS. 3A and 3B depict example screenshots of user
interfaces of a user device utilized
in conjunction with a payment instrument management system, in accordance with
some
implementations.
[0013] FIG. 4A is a flowchart that depicts an example method for dynamic and
predictive
adjustment of payment attributes based on contextual data and metadata, in
accordance with some
implementations.
[0014] FIG. 4B is a flowchart that depicts an example method to update a
payment attribute
of a payment instrument, in accordance with some implementations.
[0015] FIG. 5 is a flowchart that depicts another example method to
update a payment attribute
of a payment instrument, in accordance with some implementations.
[0016] FIG. 6 depicts an example graph that illustrates a fuel
likelihood score as a function of
time and events, in accordance with some implementations.
[0017] FIG. 7 is a block diagram that illustrates example fuel
likelihood score calculation, in
accordance with some implementations.
[0018] FIG. 8 is a diagram of an example system architecture to
provide high availability real-
time authorization of transactions, in accordance with some implementations
[0019] FIG. 9A is an example method for high availability real-time
authorization of
transactions at an authorization control pod, in accordance with some
implementations.
[0020] FIG. 9B is an example method of high availability real-time
authorization of
transactions at a payment network (card processor), in accordance with some
implementations.
[0021] FIG. 10A illustrates an example of communication of state
and policy updates, in
accordance with some implementations.
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[0022] FIG. 1 OB is a diagram that depicts an example domain model, in
accordance with some
implementations.
[0023] FIG. 11 depicts an example workflow for policy evaluation,
in accordance with some
implementations.
[0024] FIG. 12 illustrates an example evaluation of a policy by a
policy engine, in accordance
with some implementations.
[0025] FIG. 13 is a block diagram illustrating an example computing
device, in accordance
with some implementations.
DETAILED DESCRIPTION
[0026] In the following detailed description, reference is made to
the accompanying drawings,
which form a part hereof In the drawings, similar symbols typically identify
similar components,
unless context dictates otherwise. The illustrative embodiments described in
the detailed
description, drawings, and claims are not meant to be limiting. Other
embodiments may be
utilized, and other changes may be made, without departing from the spirit or
scope of the subject
matter presented herein. Aspects of the present disclosure, as generally
described herein, and
illustrated in the Figures, can be arranged, substituted, combined, separated,
and designed in a
wide variety of different configurations, all of which are contemplated
herein.
[0027] References in the specification to "some embodiments", "an embodiment",
"an
example embodiment", etc., indicate that the embodiment described may include
a particular
feature, structure, or characteristic, but every embodiment may not
necessarily include the
particular feature, structure, or characteristic. Similarly, references in the
specification to "some
implementations", "an implementation", "an example implementation", etc.,
indicate that the
implementation described may include a particular feature, structure, or
characteristic, but every
implementation may not necessarily include the particular feature, structure,
or characteristic.
Moreover, such phrases are not necessarily referring to the same embodiment or
implementation.
Further, when a particular feature, structure, or characteristic is described
in connection with an
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embodiment, such feature, structure, or characteristic may be implemented in
connection with
other embodiments whether or not explicitly described.
[0028] Businesses commonly provide payment instruments to their employees for
use with
purchases. The payment instruments can include credit cards, digital wallets,
tokens, etc. Fraud
and misuse of the credit cards is common and can lead to unnecessary
expenditure.
[0029] Monitoring usage of the payment instruments such as credit
cards poses challenges,
particularly when the expenditures are remotely incurred. For example,
businesses that maintain
fleets of vehicles, e.g., service providers of services such as plumbing,
electrical services, moving
companies, trucking operators, etc., typically incur expenses that are
incurred away from a central
place of operation. For example, fueling costs, meal costs while on travel,
etc. can be difficult to
monitor.
[0030] Techniques of this disclosure can be applied to payment
instruments such that a specific
context of a user can be monitored, and expenses can be dynamically approved
(authorized) based
on the detected user context. In some implementations, the payment instruments
may be
configured such that they are unusable at a merchant or other location by
default, and usable only
based on a signal or message transmitted to a payment network that adjusts one
or more payment
related attributes. The techniques may be applied to open payment network
systems with minimal
changes to elements in the payment infrastructure, e.g., a card processing
workflow, merchant
processing workflow, etc. This may be provided advantageous over closed
payment network
systems where the payment instrument is only valid for use at selected
merchant establishments.
[0031] Adjustment of payment attributes may be utilized to
implement smart spending controls
by only enabling transactions in categories that are pre-approved, e.g., by an
employer. For
example, dynamic adjustment of payment attributes may be utilized to only
permit transactions
in particular merchant category codes (MCC), or only for selected merchants.
Additionally,
spending limits may also be placed for different categories of transactions.
[0032] In some implementations, an authorization (auth) threshold
may be specified, which
may continuously be updated such that they are pre-calculated before an actual
authorization
request arrives in a Balance Enquiry workflow. This may enable the workflow to
be successfully
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performed within an allocated time budget, whereas the allocated time budget
may not be
sufficient for a computation of an authorization threshold in real-time (or
substantially in real-
time).
[0033] Adjustment of payment attributes may be utilized in a
variety of payment systems. For
example, some payment systems may be configured such that when a request for
authorization
for a transaction is received from a merchant at a payment network, e.g., a
card processor, the
request and details of the proposed transaction is transmitted to a computer
associated with an
issuer of the payment instrument (or a designated manager acting on behalf of
the issuer) for
approval or denial of the transaction.
[0034] In some other payment systems, policies and rules may be provided to
the payment
network, e.g., by an issuer, and each received transaction is evaluated based
on the policies and/or
rules to either approve or deny the transaction.In the interest of
efficienttransaction processing,
an outer (or upper) bound is usually specified; in some implementations, the
time available (or
made available) for approving or denying transactions is typically limited,
e.g., 5 seconds, 7
seconds, etc.
[0035] Techniques of this disclosure enable dynamic monitoring of a
user context based on
received data, and thereby enable pre-processing of data that is relevant to
approval and/or denial
of transactions. Dynamic and predicted adjustment of payment attributes
ensures that a portion of
the computations, rules evaluation, policy evaluation, and/or decision
workflows are performed
well-ahead of the transaction request itself. Such design of the payment
processing workflow can
ensure that the limited time budget to provide authorization can be met.
[0036] User context may be utilized to further dynamically adjust
the payment attributes based
on a detected user context. Context states are determined based on data that
is automatically
received from one or more user devices, e.g., mobile phones, tablets, on-board
computers,
transponders fitted on vehicles, computer systems, etc.
[0037] In some implementations, a software application (App) may be
installed on a user
device to enable determination of user context. The App may be configured such
that it is either
always-on, or may be activated during a time when a user intends to undertake
a financial
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transaction. The software application may be provided with suitable user
permissions to enable
context monitoring, collection of data elements from one or more sensors,
transmit data elements
and metadata elements to a payment instrument management system, etc.
[0038] The data may include data elements, e.g., image data,
location data, etc., as well as
metadata elements, e.g., a time of image capture, a location of image capture,
a method of
authentication, etc., associated with one or more the data elements received.
[0039] In some implementations, prediction techniques such as
Kalman filtering, Wiener
filtering, etc., may be utilized to determine future states of one or more
parameters being tracked
based on a current state and previous states of the parameter(s).
[0040] In some implementations, a current state and previous states
may be provided to a
machine learning model (ML) in order to determine a predicted state of one or
more parameters.
For example, in a fleet payment instrument management solution, a clustering
algorithm may be
applied to tuples of data that include location data, timestamp data, driver
data fuel event data
vehicle data, vehicle load data, etc. to determine a likelihood of a fuel
event for a particular vehicle
on a trip.
[0041] The ML model may be trained based on historical data.
Historical data may be
organizational (e.g., other employees within an organization), industry
specific (e.g., across
companies within the same industry), and/or across organizations/industry
(e.g., aggregated data).
The system may also consider preferences, patterns, or settings configurable
by each user and/or
company. The input data to the clustering algorithm may include historical
data from previous
trips of the driver, data from trips undertaken by other vehicles in the
fleet, and aggregated data
from other fleets.
[0042] In some implementations, a transaction likelihood score may
be calculated that may be
indicative of a degree of validity of a future transaction. A high score for a
future transaction may
indicate a high degree of validity, and thus a low likelihood of fraud or
misuse, whereas a low
transaction likelihood score may be indicative of a low degree of validity and
thus a higher chance
of being a fraudulent or misused transaction.
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[0043] In some implementations, based on a computed transaction
likelihood score, one or
more payment attributes may be updated and/or adjusted, e.g., at a computing
device associated
with a payment network. In some implementations, a policy or rule may be
adjusted or updated
based on a computed transaction likelihood score.
[0044] For example, a payment instrument may be configured such that it is not
valid for use
with any merchant category code (MCC), and thus be configured in an inactive
state. Based on a
computed transaction likelihood score, one or more MCC codes may be turned on
(e.g., approved
for transactions), and one or more transaction limits may be adjusted.
[0045] Approval of transactions may have a very limited time budget, and may
present
computational challenges in the accurate determination of a user context
state. Techniques
described herein may be utilized to predict context states accurately, and
mitigate misuse and
fraudulent transactions, while still meeting a provided computational and time
budget. A high
availability architecture for transaction processing and providing
authorizations for transactions
is also described.
[0046] The techniques described herein also increase transaction
security by detecting a
likelihood of fraud even before a fraudulent transaction is undertaken, which
provides significant
advantages over detection of fraud, post-transaction.
[0047] FIG. 1 is a diagram of an example system environment for dynamic and
predictive
adjustment of payment attributes based on contextual data and metadata, in
accordance with some
implementations.
[0048] As depicted in FIG. 1, the system environment includes a
payment instrument
management system 110, which is connected via network 160 to a fleet
management system 120,
and one or more payment networks 140.
[0049] User devices 130a - 130n and merchant computer systems 150a -
150n are also
connected to the network and are configured to transmit and receive signals
and/or messages from
one or more of fleet management system 120, payment network 140, and payment
instrument
management system 110.
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[0050] A user device 130 a - 130n can be any computer system or device used by
a person or
entity seeking to provide transaction information to a merchant computer
system, a fleet
management system, or a payment instrument management system. Thus, the user
device 130
may be represented by a laptop or desktop computer 130a, a user mobile device
130n, or one or
more other types of user devices (e.g., on-board computers, wearable devices,
tablet computers,
etc.). There may be more or fewer user devices than those shown in FIG. 1, as
represented by
ellipses. A user can be any person, whether a person (e.g., employee) or
organization, that enters
or otherwise provides transaction/transaction data, authentication data,
location data, etc. The user
devices 130 a - 130n can communicate with a network 160 to transmit (send) or
receive data or
other communications to/from any of the coupled systems and/or computing
devices.
[0051] The merchant computer systems 150a - 150n are associated
with vendors and
merchants that provide services/goods to users (e.g., fuel stations, charging
stations, repair
stations, vehicle wash stations, airlines, restaurants, hotels, car rentals,
transportation services,
etc.). The merchant computer systems 150a- 150n may be associated with a
particular computer
system or device, such as a point of service (POS) terminal device that
processes transactions for
the merchants.
[0052] Fleet management system 120 may be any combination of computer systems
utilized
by a fleet for the management of its operations, e.g., to provide route and
dispatch data via
network 160 to the payment instrument management system 110.
[0053] Payment network I 40 may be a combination of computer systems
associated with one
or more payment networks that process transactions and payments.
Communications between
payment network systems 140 and a payment instrument management system 110 may
be
implemented via network 160 as well as via direct connections.
[0054] Network 160 can be any distributed processing network used to
communicate
information between two or more computer systems. The network 160 may include
any type of
known communication medium or collection of communication media and may use
any type of
protocols to transport messages between devices. In some implementations, the
network 160 may
also represent phone systems or other means of communicating information from
a user device
to the payment instrument management system 110 and/or other components, from
the merchant
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computer systems 150 to payment instrument management system 110 and/or other
components,
from the payment networks 140 to the payment instrument management system 110
and/or other
components. Thus, the network 160 can represent systems or networks for
completing
transactions or other types of communication systems. The network 160 can be
an intranet, the
Internet, the World Wide Web, etc. In other embodiments, the network 160 may
be a public
switched telephone network (PSTN) or other type of phone system.
[0055] The network 160, in some embodiments, may also include a WAN or LAN.
Alternatively, or additionally, the network 160 may include one or more
devices that are not
administered by the same entity administering the payment instrument
management system 110.
The Internet is an example of the network 160 that constitutes an IP network
consisting of many
computers, computing networks, and other communication devices located all
over the world,
which are connected through many telephone systems and other means. Other
examples of the
network 160 include, without limitation, a standard Plain Old Telephone System
(POTS), an
Integrated Services Digital Network (ISDN), the Public Switched Telephone
Network (PSTN), a
cellular network, and any other type of packet-switched or circuit-switched
network known in the
art.
[0056] FIG. 2 depicts an example payment instrument management system, in
accordance with
some implementations.
[0057] An example implementation of a payment instrument management system is
described
in conjunction with FIG. 2, which is representative of any system or
collection of systems in
which the various applications, services, scenarios, and processes disclosed
herein may be
implemented. Payment instrument management system 110 may be employed to
execute a
payment instrument management service on behalf of a financial institution, on
behalf of an
enterprise, e.g., fleet management system, such as the one described herein.
[0058] The payment instrument management system 110 may include a number of
processor
readable software modules that, when executed by the processor, perform
functions described
herein. The modules may also be implemented on one or more distributed
computing systems,
and instances, processes, and/or functions may be utilized to perform the
described functions
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[0059] The payment instrument management system 110 includes an authentication
module
204 that securely authenticates users, vehicles, vendors, companies, etc.,
using one or more
single-factor or multi-factor authentication indicia. Exemplary indicia
comprise something the
object or knows, something the object has, and something the object is.
[0060] The payment instrument management system 110 includes a pre-
validation module 206
that determines a likelihood of a future transaction based on context data.
The pre-validation
module may include a machine learning module that learns from previous
transactions and creates
and augments context data, rules, and/or learnings to continually improve
accuracy of the
payment instrument management system. The pre-validation system can also
include a context
module that can apply deterministic rule sets and heuristic rule sets. The
payment instrument
management system 110 includes a rules module 214 that is utilized for
resolving rules and a
policy module 216.
[0061] The payment instrument management system 110 includes a communication
module
210 that enables inter-device communications between the payment instrument
management
system 110 and devices associated with the payment networks, fleet management
systems,
financial institutions, merchants, companies, and any connected databases. The
various modules
are in communication via a signal transmission medium, such as a bus, local
network, wide area
network, and the like.
[0062] The communication module 210, when executed by the processor, may
enable the
payment instrument management system 110 to communicate with the other devices
in the system
100. For instance, the communication module 210 may be configured to
modulate/demodulate
communications exchanged over the network 160, determine timings associated
with such
communications, determine addresses associated with such communications, etc.
In some
embodiments, the communication module 210 may be configured to allocate
communication
ports of the transaction monitor 101 for use as a communication interface. The
communication
module 210 may further be configured to generate messages in accordance with
communication
protocols used by the network 160 and to parse messages received via the
network 160.
The payment instrument management 110 includes a transaction module 208 for
processing
transactions and a settlement module 212 for processing transaction settlement
data. A misuse and
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fraud detection module 218 is included for detecting fraudulent and misuse
activity using a
payment instrument based on detected data patterns.
[0063] The payment instrument management system 110 includes a
financial institution
module 220 to interact with financial institutions that issue physical or
digital financial
instruments used to complete transactions.
[0064] A payment network module 222 may be utilized to interact with payment
networks and
process received messages and transmit messages that are utilized to make one
or more
adjustments and/or updates to payment attributes stored on devices associated
with the payment
networks. The payment instrument management system 110 includes a context
module 224 for
determining context data for transactions (including a context feedback loop).
[0065] The payment instrument management system 110 includes an approval
module 226
that approves or denies transactions received by the payment instrument
management system.
[0066] The payment instrument management system 110 may include one or more
databases,
e.g., a historical database 230, and a policy and rules database 240.
[0067] FIGS. 3A and 3B depict example screenshots of user
interfaces of a user device utilized
in conjunction with a payment instrument management system, in accordance with
some
implementations.
[0068] In some implementations, a user device, e.g., a mobile
device of a user (driver) is
utilized to obtain contextual data as well as to provide notification(s) to
the user. In this illustrative
example, techniques as applied in a fleet management context are described.
[0069] A software application of the payment (instrument)
management system is configured
to execute on the user device. The software application may be made available
to users for
download. In some implementations, installation and execution of the software
application may
be specified as a requirement for users to be able to access one or more
payment instruments.
[0070] A first screenshot (310) illustrates an example user
verification screen. In some
implementations, a user may provide their credentials, e.g., username,
password, PIN etc., to sign
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into the software application. Multiple modes of authentication, including
face recognition, and
fingerprint recognition may be utilized to perform user authentication.
[0071] A second screenshot (320) depicts an example vehicle
verification screen. In some
implementations, a user may be prompted to authenticate a vehicle being
utilized for the trip.
Authentication of the vehicle may be supported via a transponder, via an image
of a license plate,
an image of a VIN number plate, an image of an odometer, or by scanning other
indicia such as
a QR code that may be located in the vehicle, where the QR code is unique to
the vehicle.
[0072] A third screenshot (330) depicts an example of additional
verification that may be
utilized in some scenarios. An image of an odometer of the vehicle or vehicle
surroundings may
be requested via the user device. The user interface is enabled to guide the
user to take a picture
by providing a guiding frame as well as by providing directions to the user to
suitably move the
user device to obtain an image that meets certain specifications.
[0073] Per techniques of this disclosure, a fuel likelihood score
may be calculated based on
received trip data, and when the fuel likelihood score meets a predetermined
threshold, a payment
instrument associated with the user device (and user) may be pre-validated for
fuel transactions.
[0074] A fourth screenshot (340) depicts an example screenshot that
indicates successful pre-
validation. The successful pre-validation may also be depicted on a map on the
user device that
indicates approved fuel locations along a proposed route of the user.
[0075] Additional indicators (not shown) may be utilized. For
example, a green indicator (e.g.,
a green dot) on the user interface of the software application may serve as an
indicator that the
payment instrument is approved for fueling, a yellow indicator that additional
verification data is
needed before successful pre-validation of the payment instrument, and a red
indicator that pre-
validation is unlikely.
[0076] A fifth screenshot (350) depicts an example screenshot where
discount offers may be
displayed to the user via their user device. Advisories and suggestions may be
provided to a user
via the user interface based on received trip data. The advisories and/or
suggestions may originate
from the payment instrument management system, a payment management system, or
from a fleet
management system, and be displayed on the user device. A sixth screenshot
(360) depicts an
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example of a notification that may be provided. For example, if it is
determined that the vehicle
is located near the only fuel station for a certain distance from a current
location, and based on
trip data, if it is estimated that the fuel level is low, a notification may
be provided recommending
a fueling.
[0077] FIG. 4A is a flowchart that depicts an example method for dynamic and
predictive
adjustment of payment attributes based on contextual data and metadata, in
accordance with some
implementations.
[0078] In some implementations, method 400 can be implemented, for example, on
a payment
instrument management system 110 described with reference to FIG. 1. In the
described
examples, the implementing system includes one or more digital processors or
processing
circuitry ("processors"), and one or more storage devices. In some
implementations, different
components of one or more servers and/or clients can perform different blocks
or other parts of
the method 400. In some examples, a first device is described as performing
blocks of method
400. Some implementations can have one or more blocks of method 400 performed
by one or
more other devices (e.g., other client devices, over a distributed computer
system, or server
devices) that can send results or data to the first device.
[0079] In some implementations, the method 400, or portions of the
method, can be initiated
automatically by a system. In some implementations, the implementing system is
a first device.
For example, the method (or portions thereof) can be periodically performed,
or performed based
on one or more particular events or conditions, e.g., a change in a detected
user context, a signal
received from a card/payment processor, and/or one or more other conditions
occurring which
can be specified in settings read by the method.
[0080] Processing may begin at block 410.
[0081] At block 410, payment instrument data may be received that
is associated with one or
more payment instrument(s) that are associated with a particular user.
[0082] The payment instrument data can include data elements
associated with the payment
instrument, e.g., a payment instrument identifier(s), user identifier(s),
budgetary limits on the
payment instrument(s), etc.
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[0083] The payment instrument can be any type of payment
instrument, e.g., a physical card
with a card number, a virtual card, a wallet, a bank account, etc.
[0084] Block 410 may be followed by block 415.
[0085] At block 415, one or more computing devices are associated
with the user. The one or
more computing devices may include user devices, e.g., a mobile phone
associated with the user,
a desktop, tablet, or laptop computer associated with the user; storage
devices and specifically,
data structures associated with the user, etc.
[0086] Block 415 may be followed by block 420.
[0087] At block 420, contextual data and metadata associated with
the contextual data may be
received from the computing devices.
[0088] Block 420 may be followed by block 425.
[0089] At block 425, a likelihood score for a future financial
transaction is determined based
on the received contextual data and metadata.
[0090] For example, contextual data may be received from one or more user
devices, as well
as from one or more enterprise computer systems. For example, if an
organization and/or user
provides permission for the payment instrument management system to access
calendar data from
the user's calendar app, and/or other apps installed on the user's device, the
calendar data may be
used as attribute and/or context data. Contextual data may be also determined
from publicly
available information sources that may be accessed, e.g., via an application
programming
interface (API). For example, flight arrival and departure information,
traffic data, weather data,
etc., may be utilized as context data.
[0091] In some implementations, user devices and data elements and metadata
elements
received from the user device(s) may be utilized for additional context. For
example, if it is
determined that a user is traveling and not at their desk, a transaction
originating (or purporting
to originate) from their desktop computer may be unlikely to be a valid
transaction. Accordingly,
an inferred state of mobility of the user may be utilized to refine the user
context.
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[0092] Block 425 may be followed by block 430.
[0093] At block 430, it is determined whether the likelihood score
meets a predetermined
threshold, e.g., a confidence threshold.
[0094] If it is determined that the likelihood score meets the
predetermined threshold, block
430 may be followed by block 435, else, block 430 may be followed by block
420.
[0095] At block 435, one or more payment attributes or policies
associated with the payment
instrument(s) may be adjusted.
[0096] For example, an MCC code for a payment instrument may be activated only
upon a
determination of a valid and likely future transaction that utilizes the
payment instrument.
Depending on the payment architecture, e.g., in a payment network that
supports balance enquiry,
a pre-authorization amount or policy may be adjusted based on the transaction
likelihood score.
Similarly, a merchant or merchants in a certain geofenced region may be pre-
validated or pre-
approved for a transaction based on user location data and a determination
that the user is likely
to transact in the geofenced region.
[0097] Techniques described herein may be utilized to provide
faster transaction processing,
by reducing a time needed for performing certain computations and to reduce
computational loads
when the actual transaction is initiated, e.g., by a card swipe, or wallet
transaction, etc., that leads
to an authorization request transmitted.
[0098] Blocks 41 0-43 5 can be performed (or repeated) in a
different order than described
above and/or one or more steps can be omitted.
[0099] FIG. 4B is a flowchart that depicts an example method to update a
payment attribute
of a payment instrument, in accordance with some implementations.
[00100] In some implementations, method 450 can be implemented, for example,
on a payment
instrument management system 110 described with reference to FIG. 1. In the
described
examples, the implementing system includes one or more digital processors or
processing
circuitry ("processors"), and one or more storage devices. In some
implementations, different
components of one or more servers and/or clients can perform different blocks
or other parts of
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the method 450. In some examples, a first device is described as performing
blocks of method
450. Some implementations can have one or more blocks of method 450 performed
by one or
more other devices (e.g., other client devices or server devices) that can
send results or data to the
first device.
[00101] In some implementations, the method 450, or portions of the method,
can be initiated
automatically by a system. In some implementations, the implementing system is
a first device.
For example, the method (or portions thereof) can be periodically performed,
or performed based
on one or more particular events or conditions, e.g., reception of fresh trip
data, a signal received
from a user device or a payment network, and/or one or more other conditions
occurring which
can be specified in settings read by the method.
[00102] Method 450 may begin at block 454.
[00103] At block 454, an indication and/or signal of a trip being undertaken
by a vehicle may
be received by a computing device comprising a processor and memory.
[00104] In some implementations, the indication may be determined based on a
received signal
from a user device, e.g., a sign in, or be based on detection of a movement of
location of a user
device. For example, the inference may be based on receipt of data from a user
device from a first
location and a second location, at different times.
[00105] In some implementations the indication may be received from a fleet
operator computer
system. In some implementations, the indication may be received at a computer
device, from a
software application (App) executing on a user device. In some
implementations, the indication
may be transmitted based on user input at a commencement of a trip. The user
device may be
associated with a driver or user of the vehicle and the indication may include
additional data, e.g.,
identifier of a user device, vehicle identifier, etc.
[00106] In some implementations, a user and/or a vehicle may be authenticated.
For example,
a user may be authenticated using additional authentication measures, e.g.,
requiring a user to
enter an alphanumeric PIN, enter a multi-factor authentication key that is
transmitted by the
computer device to a known and trusted device associated with the user,
provide biometric data
such as fingerprint and/or facial biometric data.
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[00107] In some implementations, a vehicle may be authenticated based on a
received image of
a vehicle license plate, a QR code or other indicia located on the vehicle, a
vehicle VIN number
plate, and/or a vehicle odometer. The received images may be verified based on
previously
obtained/received and stored images on a database associated with the payment
instrument
management system.
[00108] In some implementations, an indicium that is attached to a vehicle may
be utilized for
authentication. For example, a QR code affixed to an interior of the vehicle
may be utilized. The
user may be requested to scan the QR code using their user device, which may
then be received
at the payment instrument management system based on a message transmitted by
the user device.
[00109] In some implementations, a payment instrument associated with the trip
and/or user
may also be authenticated based on accessing a database that includes stored
data about payment
instruments issued to employees or based on a user entry of a payment
instrument identifier,
and/or be combined with an image (of a physical card or other payment
instrument) or an
alphanumeric identifier or token received from an application executing on a
user device of the
user. In some implementations, previously provided (and authenticated) payment
instruments
may be associated with a particular user based on historical data records.
[00110] In some implementations, the payment instrument is configured to be in
an inactive
state, e.g., be configured such that it is not eligible for transactions
without a message/signal being
transmitted to a payment network that adjusts one or more payment attributes
of the payment
instrument. For example, an MCC code for fuel purchases may be turned off as
an initial state for
the payment instrument. In some other implementations, the payment instrument
may be
configured such that selected transactions are permitted even without an
adjustment of a payment
attribute, e.g., be pre-approved (pre-validated) for emergency supplies,
repair services, fuel
transactions up to a specified monetary limit, etc.
[00111] In some implementations, fuel related data, e.g., an initial fuel
state and/or odometer
reading of the vehicle may also be obtained and stored as a reference for the
trip. Fuel related data
may be obtained based on image recognition of an odometer and/or fuel gauge,
from a computing
device associated with a fleet management system or based on data entered by a
user via a user
interface.
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[00112] Block 454 may be followed by block 456.
[00113] At block 456, a user device and a vehicle (identifier) are associated
by the computer
device with the trip.
[00114] In some implementations, the user device and a vehicle identifier are
associated with
the trip based on the received and authenticated data, whereas in some
implementations,
previously received route data and/or historic data may also be utilized for
the association.
[00115] Subsequent to their association, data received from the user device is
utilized as a
surrogate for vehicle data, unless conflicting data is received.
[00116] Block 456 may be followed by block 458.
[00117] At block 458, trip data that includes at least location data obtained
from the user device
is received by the computer device. In some implementations, the trip data is
received from a
software application (App) executing on a user device of a user of the
vehicle.
[00118] In some implementations, transmission of the trip data may be
configured such that it
may be received at a predetermined periodicity (time-interval). The
periodicity may be
dynamically adjusted based on a time and/or distance elapsed since the
commencement of the
trip. Adjustable periodicity may help conserve battery life for the user
device by not requiring
frequent transmission of data and maintaining network connectivity during the
trip.
[00119] In some implementations, trip data may be collected at a first
frequency locally at the
user device and transmitted by the user device at a second (e.g., a lower)
frequency. Additionally,
the software application may be configured such that sensors on the user
device are also activated
only when data is to be obtained. For example, a GPS sensor on the user device
may be configured
such that it is activated only when location data is to be obtained from the
user device, thereby
extending battery life of the user device.
[00120] In some implementations, the trip data may be received as a plurality
of (data) packets
transmitted from the application at a first periodicity that is based on a
time elapsed since an
estimated time of commencement of the trip and/or a distance elapsed since the
commencement
of the trip. In some implementations, the periodicity may be based on a user
device battery status.
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[00121] In some implementations, the trip data is received based on a
particular periodicity, or
based on an event threshold, e.g., a certain mileage being driven. In some
implementations, the
trip data is received as a response to a ping transmitted from the payment
instrument management
system.
[00122] In some implementations, if a ping of the user device does not elicit
a response, a
specified pre-authorization may be communicated to a payment network based on
an agreement
or arrangement with the fleet manager/fleet management system setting.
[00123] In some implementations, the trip data may include one or more of
location data,
accelerometer data, connectivity data such as an identity of a network
operator, speed/velocity
data, battery status of a user device, etc.
[00124] In some implementations, the trip data may be a combination of
automatically sensed
data and user entered data. For example, a user may initiate a request to pre-
validate a future
transaction that is received at the payment instrument management system, and
the received
request may include one or more of a requested budget or fuel amount, a
current fuel status,
location information, average mileage during the trip, an intended fuel pump
location or other
fuel pump details.
[00125] The trip data may also include one or more images, metadata associated
with the one
or more images, and metadata associated with the user device. For example, the
one or more
images may include an image of an odometer and/or a dashboard of the vehicle.
[00126] In some implementations, a software application may be configured to
collect and store
trip data, and subsequently transmit for reception by the payment instrument
management system.
For example, during a time when a user device does not have network
connectivity, the software
application may be configured to operate in an offline mode, and monitor a
user context, obtain
data elements and metadata elements for transmitting to the payment instrument
management
system upon restoration of user device connectivity.
[00127] In some implementations, in an offline mode, the software application
may display on
the user device a message about locations on the route where network
connectivity is available
(based on previously received trip data and/or historic data). For example,
the network
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connectivity at a location on the route may be provided by an operator of an
establishment such
as a fuel station, e.g., Wifi, Bluetooth, NFC, or other connectivity provided
at the location where
the user device may be utilized to provide additional data elements and
metadata elements to
enable pre-validation before a transaction.
[00128] In some implementations, the payment instrument management system may
verify,
based on historical data, that a network operator associated with the user
device (cellular operator,
roaming partner of the cellular operator, etc.) does not have network coverage
at a particular
location. Upon restoration of network connectivity, data already captured on a
software
application may be transmitted to the payment instrument management system.
[00129] In some implementations, during conditions where the user device has
limited or no
network connectivity, instructions may be provided to the user (in offline
mode) to proceed to
enter payment instrument data, e.g., card number, wallet data, tokens, etc. at
a fuel pump. In such
implementations, transaction approval may be provided by the payment network,
e.g., for a
limited authorization amount. In some implementations, it may be confirmed
that connectivity
for a network operator associated with the user device operator is indeed
limited or not available,
e.g., based on user device history of connected networks, or confirmation of a
low battery state
of the user device.
[00130] Block 458 may be followed by block 460.
[00131] At block 460, a fuel likelihood score for the first trip is calculated
based on the received
trip data. The fuel likelihood score is indicative of a degree of validity of
a fuel transaction. In
some implementations, the fuel likelihood score may be a confidence score
associated with a valid
future transaction based on the received trip data, e.g., current mileage, the
metadata associated
with one or more images, location, time of request, etc.
[00132] The fuel likelihood score may be computed using different
methodologies. In some
implementations, the fuel likelihood score is calculated, based on a
determination, at a computing
device of the payment instrument management system, of a traveled distance, a
quantity of fuel
consumed, a quantity of fuel remaining in the vehicle, and/or an expected fuel
quantity. The
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determination may be based on the received trip data, e.g., received location
data, received
image(s) of an odometer, etc.
[00133] In some implementations, calculating the fuel likelihood score may
include analyzing
the received image data and the received image metadata to determine one or
more of an odometer
mileage, an estimated fuel level, and/or a distance traveled since a previous
fueling event.
[00134] In some implementations, received data elements and metadata elements
may be
verified. For example, a time and location of capture of a received odometer
image may be
verified against a time and location of transmission of the data; it may be
verified that a received
odometer image matches an odometer image previously received by matching its
background.
Similarly, pixel subtraction may be utilized using a previous odometer image
to verify the
odometer image and to enable faster extraction of mileage data from the
odometer image.
[00135] In some implementations, the received trip data may be supplemented by
telemetric
data from a transponder associated with the vehicle. The telemetric data may
include one or more
data elements, e.g., location data, fuel level data, speed data, and fuel
efficiency data.
[00136] In some implementations, calculating the fuel likelihood score may
include applying a
trained machine learning (M_,) model to the trip data.
[00137] Features such as driver information, driver history, driver credit
score, average mileage,
last known location, connection history at location, a method of
authentication, e.g., biometric
authentication, facial recognition based authentication, fingerprint based
authentication, PIN
based authentication, etc., a fuel state at a start of the trip, a time
elapsed, a vehicle type, etc. ,
may be provided to the ML model to enable determination of the fuel likelihood
score, and a
corresponding confidence interval for the score. Additional details of fuel
likelihood score(s) are
described with reference to FIG. 7.
[00138] Block 460 may be followed by block 462.
[00139] At block 462, it is determined whether the fuel likelihood score for
the trip and/or
vehicle meets a first threshold.
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[00140] In some implementations, the first threshold may be an indicator that
the vehicle
associated with a trip has a relatively high likelihood of needing to be
fueled.
[00141] If it is determined that the fuel likelihood meets the first
threshold, block 462 may be
followed by 464, else block 462 may be followed by block 468.
[00142] At block 464, based on a determination that the fuel likelihood score
meets the first
threshold, a message, e.g., over a http or https channel, may be transmitted
to a computer
associated with a payment network to update one or more payment attributes
associated with the
payment instrument. The payment attributes may be attributes, parameters, and
or configuration
settings that are stored in a device of the payment network.
[00143] For example, a message to unblock an MCC code for a fuel category may
be transmitted
to a computer associated with a payment network, based on the fuel likelihood
score meeting a
predetermined first threshold.
[00144] In some implementations, the payment attributes may be adjusted only
for pumps in
the direction of travel (based on an extrapolation of current trip data) that
matches route
information transmitted to the payment instrument management system.
[00145] Block 464 may be followed by block 466.
[00146] At block 466, an indication that the payment instrument is approved
for a future fuel
transaction is displayed on an application executing on the user device. For
example, a list of
approved fuel pumps (e.g., indicated by a check mark, or highlighted in a
particular color) may
be displayed on a map along with a current location.
[00147] In some implementations, the indication may be a notification (e.g., a
notification code)
transmitted to the user device, which is displayed on a user interface of the
user device by the
software application executing on the user device.
[00148] In some implementations, the indication may be a text message that is
transmitted to
the user device, e.g., over a cellular network. In some implementations, the
indication may be
displayed on an in-vehicle display.
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[00149] Subsequent to the transmission of a message to the payment network, an
authorization
request may be received from the payment network for a transaction, e.g., a
fuel transaction. For
example, a request may be received at two or more groups of authorization
control pods associated
with the payment instrument management system, an authorization request from
the payment
network for a fuel transaction, wherein the authorization request includes an
identifier associated
with the payment instrument and transaction information about the transaction.
Each
authorization control pod may include a processing service, a database
service, and a policy agent.
[00150] The received request and the transaction information may be analyzed,
at a
corresponding policy agent of a single authorization control pod of the groups
of authorization
control pods by comparing one or more transaction attributes in the
transaction information
combined with contextual and historical (historic) data against one or more
policies stored in the
database service. Based on the comparison, one of an approval or a rejection
of the transaction
may be transmitted (returned) to the payment network (e.g., a card processor).
[00151] In some implementations, the transaction attributes in the transaction
information
received from the payment network may include a pump location identifier, and
analyzing the
transaction information may include comparing the pump location identifier to
the location data
received from the user device. In some implementations, the transaction may be
authorized only
when there is a match of the pump location identifier with the location data
received from the
user device.
[00152] At block 468, based on a previous determination that the fuel
likelihood score does not
meet the first threshold, it is determined whether the fuel likelihood score
meets a second
threshold. In some implementations, the second threshold may be advantageously
utilized in
situations where additional trip data would enable a refinement (updating) of
a fuel likelihood
score, and the calculation of a more accurate fuel likelihood score.
[00153] If it is determined that the fuel likelihood score does not meet the
first threshold and
meets a second threshold, block 468 may be followed by block 470, else block
468 may be
followed by block 482.
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[00154] At block 470, a request for additional verification data is displayed
or caused to be
displayed on the application executing on the user device. The request may be
displayed or caused
to be displayed on the user device and may include a request for a capture and
transmission of an
image of one or more of an odometer, a dashboard, or a license plate of the
vehicle.
[00155] In some implementations, the request may include a request for an
image of a license
plate of the vehicle and/or an image of an environment surrounding the
vehicle, e.g., an image of
a fuel pump, an image that shows other vehicles, an image that depicts a front
and a rear of the
vehicle, etc.
[00156] Block 470 may be followed by block 472.
[00157] At block 472, additional verification data may be received from the
user device and
may include the additional verification data, e.g., image data and
corresponding image metadata.
[00158] Block 472 may be followed by block 474.
[00159] At block 474, an updated fuel likelihood score for the trip is
calculated based on the
additional verification data and the received trip data. The additional
verification data received
may be utilized to generate an updated location and/or an updated fuel state
for the vehicle.
[00160] Block 474 may be followed by block 476.
[00161] At block 476, the updated fuel likelihood score is compared to the
first threshold.
[00162] If it is determined that the updated fuel likelihood score meets the
first threshold, block
476 may be followed by block 478, else block 476 may be followed by block 458.
[00163] At block 478, a signal/message is transmitted to a computer associated
with a payment
network, to update one or more payment attributes that are stored in a device
of the payment
network and wherein the one or more payment attributes are associated with the
payment
instrument.
[00164] Block 478 may be followed by block 480.
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[00165] At block 480, an indication that the payment instrument is approved
for the fuel
transaction may be displayed or caused to be displayed. The indication may be
provided on an
application executing on the user device.
[00166] At block 482, a second status indicator or notification may be
displayed on the user
device specifying that the payment instrument is not pre-validated for a
transaction. The user may
be prompted to call a customer number in such a scenario. Block 482 may be
followed by block
458.
[00167] A fuel likelihood score falling below a third threshold may be
utilized as an indicator
of more serious fraud issues, such as vehicle theft, etc. For example, the
trip data received from a
user device and/or vehicle transponder may indicate a vehicle location that is
not inside a defined
geofence. In such a scenario, an alert may be issued, or the vehicle be locked
by transmitting a
message to a vehicle controlling system.
[00168] Blocks 454-482 can be performed (or repeated) in a different order
than described
above and/or one or more steps can be omitted.
[00169] FIG. 5 is a flowchart that depicts another example method to update a
payment attribute
of a payment instrument, in accordance with some implementations.
[00170] In some implementations, method 500 can be implemented, for example,
on a payment
instrument management system 110 described with reference to FIG. 1. In the
described
examples, the implementing system includes one or more digital processors or
processing
circuitry ("processors"), and one or more storage devices. In some
implementations, different
components of one or more servers and/or clients can perform different blocks
or other parts of
the method 500. In some examples, a first device is described as performing
blocks of method
500. Some implementations can have one or more blocks of method 500 performed
by one or
more other devices (e.g., other client devices or server devices) that can
send results or data to the
first device.
[00171] In some implementations, the method 500, or portions of the method,
can be initiated
automatically by a system. In some implementations, the implementing system is
a first device.
For example, the method (or portions thereof) can be periodically performed,
or performed based
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on one or more particular events or conditions, e.g., an indication of
commencement of a trip, trip
data received from a user device, a transaction request, a signal or message
received from a
payment network computer, and/or one or more other conditions occurring which
can be specified
in settings read by the method.
[00172] Method 500 may begin at block 505.
[00173] At block 505, route data for a plurality of trips may be received at a
computing device.
The route data may include a corresponding geofence region, a corresponding
user (or driver)
identifier, a corresponding payment instrument identifier, and a corresponding
vehicle identifier
for each trip of the plurality of trips.
[00174] In some implementations, the route data may include dispatch data that
includes a
planned route, an expected time of commencement of the trip, a vehicle
identifier of a vehicle
intended for use on the trip, and a user/driver for each trip of the route for
a fleet that includes a
plurality of vehicles.
[00175] In some implementations, a corresponding geofence may be specified by
a combination
of an origin location, a destination location, one or more intermediate
locations, and one or more
proposed routes of travel.
[00176] In some implementations, a corresponding geofence may be specified by
specifying a
particular region of travel, e.g., a specified area that may include one or
more locations and an
area surrounding the locations. For example, in some implementations, the
geofence may be
characterized by a polygon, a circle, or an irregular shape that includes one
or more locations
defined by a latitude and longitude and defined by a perimeter of the
geofenced region.
[00177] Block 505 may be followed by block 510.
[00178] At block 510, a transmission of one or more data packets may be
received from a user
device, e.g., a mobile smartphone, a laptop computing device, a vehicle on-
board computer, etc.
The one or more data packets may be generated at a software application
executing on the user
device, wherein the one or more data packets comprise a user identifier. In
some implementations,
a mode of authentication of the user device may also be obtained. In some
implementations, the
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one or more data packets may include data elements as well as metadata
elements associated with
corresponding data elements.
[00179] Block 510 may be followed by block 515.
[00180] At block 515, a user identifier and a vehicle identifier may be
authenticated based on
the received user and vehicle data and matching against previously received
credentials and/or
identifiers.
[00181] For example, a first vehicle may be identified based on a match of the
received vehicle
identifier with the received route data. A mobile device identifier and/or a
user identifier may also
be associated with a vehicle identifier, based on data entry or an image of a
license plate or VIN
number or from a previous association of a particular user (mobile) device
with the vehicle.
[00182] The first trip data and any received telemetric data may be stored in
a linked data
structure for future authentication and for training a machine learning (ML)
model.
[00183] Block 515 may be followed by block 520.
[00184] At block 520, a first trip of the plurality of trips may be identified
based on a match of
the authenticated user identifier and the authenticated vehicle identifier
with the route data. In
some implementations, the first trip may additionally be associated with a
first payment
instrument.
[00185] Block 520 may be followed by block 525.
[00186] At block 525, trip data is received from the authenticated user
device.
[00187] The first trip data may be received as a plurality of packets
transmitted from the
software application at a first periodicity that is based on one or more of a
time elapsed since an
estimated time of commencement of the trip and/or a distance elapsed since the
commencement
of the trip.
[00188] Block 525 may be followed by block 530.
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[00189] At block 530, a fuel likelihood score is calculated for the trip. The
fuel likelihood score
is indicative of a degree of validity of a fuel transaction. In some
implementations, the fuel
likelihood score may be a confidence score associated with a valid future
transaction based on the
received trip data, e.g., current mileage, the metadata associated with one or
more images,
location, time of request, etc.
[00190] The fuel likelihood score may be computed using different
methodologies. In some
implementations, the fuel likelihood score is calculated, based on a
determination, at a computing
device of the payment instrument management system, of a traveled distance, a
quantity of fuel
consumed, a quantity of fuel remaining in the vehicle, and/or an expected fuel
quantity. The
determination may be based on the received trip data, e.g., received location
data, received
image(s) of an odometer, etc.
[00191] In some implementations, calculating the fuel likelihood score may
include analyzing
the received image data and the received image metadata to determine one or
more of an odometer
mileage, an estimated fuel level, and/or a distance traveled since a previous
fueling event.
[00192] In some implementations, received data elements and metadata elements
may be
verified. For example, a time and location of capture of a received odometer
image may be
verified against a time and location of transmission of the data; it may be
verified that a received
odometer image matches an odometer image previously received by matching its
background.
Similarly, pixel subtraction may be utilized using a previous odometer image
to verify the
odometer image.
[00193] In some implementations, the received trip data may be supplemented by
telemetric
data from a transponder associated with the vehicle. The telemetric data may
include one or more
data elements, e.g., location data, fuel level data, and fuel efficiency data.
[00194] In some implementations, calculating the fuel likelihood score may
include applying a
trained machine learning (ML) model to the trip data.
[00195] Features such as driver information, driver history, driver credit
score, average mileage,
last known location, connection history at location, a method of
authentication, e.g., biometric,
face based, fingerprint based, PIN based, etc., a fuel state at start, a time
elapsed, a vehicle type,
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etc. , may be provided to the ML model to enable determination of the fuel
likelihood score, and
a corresponding confidence interval for the score. Additional details of fuel
likelihood score(s)
are described with reference to FIG. 7.
[00196] Block 530 may be followed by block 535.
[00197] At block 535, it is determined whether the fuel likelihood score for
the trip and/or
vehicle meets a first threshold. If it is determined that the fuel likelihood
score for the trip and/or
vehicle meets the first threshold, block 535 may be followed by block 540,
else block 535 may
be followed by block 550
[00198] At block 540, based on a determination that the fuel likelihood score
meets the first
threshold, a message may be transmitted, e.g., over a http or https channel,
to a computer
associated with a payment network to update one or more payment attributes
associated with the
payment instrument. The payment attributes may be attributes, parameters,
and/or configuration
settings that are stored in a device of the payment network.
[00199] For example, a message may be transmitted from the processor to a
computer
associated with a payment network, wherein the message includes a request or
command to
unblock a merchant category code (MCC) for a fuel category based on the fuel
likelihood score
meeting a predetermined first threshold.
[00200] Block 540 may be followed by block 545.
[00201] At block 545, an indication that the payment instrument is approved
for a future fuel
transaction is displayed on an application executing on the user device. For
example, a list of
approved fuel pumps (e.g., indicated by a check mark, highlighted in a
particular color, etc.) may
be displayed on a map along with a current location.
[00202] In some implementations, the indication may be a notification (e.g., a
notification code)
transmitted to the user device, which is displayed on a user interface of the
user device by the
software application executing on the user device.
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[00203] In some implementations, the indication may be a text message that is
transmitted to
the user device, e.g., over a cellular network. In some implementations, the
indication may be
displayed on an in-vehicle display.
[00204] Subsequent to the transmission of a message to the payment network, an
authorization
request may be received from the payment network for a transaction, e.g., a
fuel transaction. For
example, a request may be received at two or more groups of authorization
control pods associated
with the payment instrument management system, an authorization request from
the payment
network for a fuel transaction, wherein the authorization request includes an
identifier associated
with the payment instrument and transaction information about the transaction.
Each
authorization control pod may include a processing service, a database
service, and a policy agent.
[00205] The received request and the transaction information may be analyzed,
at a
corresponding policy agent of a single authorization control pod of the groups
of authorization
control pods by comparing one or more transaction attributes in the
transaction information
combined with contextual and historical (historic) data against one or more
policies stored in the
database service. Based on the comparison, one of an approval or a rejection
of the transaction
may be transmitted (returned) to the payment network (e.g., a card processor).
[00206] In some implementations, the transaction attributes in the transaction
information
received from the payment network may include a pump location identifier, and
analyzing the
transaction information may include comparing the pump location identifier to
the location data
received from the user device.
[00207] At block 550, based on a previous determination that the fuel
likelihood score does not
meet the first threshold, it is determined whether the fuel likelihood score
meets a second
threshold.
[00208] If it is determined that the fuel likelihood score does not meet the
first threshold and
meets a second threshold, block 550 may be followed by block 555, else block
550 may be
followed by block 585.
[00209] At block 555, a request for additional verification data is displayed
or caused to be
displayed on the application executing on the user device.
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[00210] The request may be displayed or caused to be displayed on the user
device and may
include a request for a capture and transmission of an image of one or more of
an odometer, a
dashboard, or a license plate of the vehicle.
[00211] In some implementations, the request may include a request for an
image of a license
plate of the vehicle and/or an image of an environment surrounding the
vehicle, e.g., an image of
a fuel pump, other vehicles, etc.
[00212] Block 555 may be followed by block 560.
[00213] At block 560, additional verification data may be received from the
user device and
may include the additional verification data, e.g., image data and
corresponding image metadata.
[00214] Block 560 may be followed by block 565.
[00215] At block 565, an updated fuel likelihood score for the trip is
calculated based on the
additional verification data and the received trip data. The additional
verification data received
may be utilized to generate an updated location and/or an updated fuel state
for the vehicle, and
to generate the updated fuel likelihood score based on the updated location
and/or fuel state.
[00216] Block 565 may be followed by block 570.
[00217] At block 570, the updated fuel likelihood score is compared to the
first threshold.
[00218] If it is determined that the updated fuel likelihood score meets the
first threshold, block
570 may be followed by block 575, else block 570 may be followed by block 525.
[00219] At block 575, a signal/message is transmitted, e.g., via an API, or
via a http/https
channel, to a computer associated with a payment network, to update one or
more payment
attributes that are stored in a device of the payment network and wherein the
one or more payment
attributes are associated with the payment instrument.
[00220] Block 575 may be followed by block 580.
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[00221] At block 580, a first status indicator, e.g., an indication that the
payment instrument is
approved for the fuel transaction may be displayed or caused to be displayed.
The indication may
be provided on an application executing on the user device.
[00222] Block 580 may be followed by block 525.
[00223] At block 585, a second status indicator or notification may be
displayed on the user
device specifying that the payment instrument is not pre-validated for a
transaction. The user may
be prompted to contact an issuer or manager associated with the payment
instrument.
[00224] Blocks 505 - 585 can be performed (or repeated) in a
different order than described
above and/or one or more steps can be omitted.
[00225] For example, in some implementations, only a first threshold may be
utilized, or only
a second threshold may be utilized. For example, in some implementations, all
pre-validation may
require the transmission of verification data (by the user device) for
reception at the payment
instrument management system. In some other implementations, only automatic
pre-validation
may be performed, e.g., pre-validation is only performed based on
automatically sensed and
transmitted data, and data entered by users is not considered.
[00226] In some implementations, block 580 may be performed before block 575.
[00227] FIG. 6 depicts an example graph that illustrates a fuel likelihood
score as a function of
time and events, in accordance with some implementations.
[00228] In this illustrative example, fuel likelihood scores calculated for an
example trip are
depicted on a graph 600 that includes time 605 as its X-axis, and the fuel
likelihood score 610 as
its Y-axis. The graph also depicts a first threshold (680) and a second
threshold (690).
[00229] In some implementations, the first threshold is a configurable
parameter that is
indicative of a fuel likelihood score level greater than which, a fuel
transaction event is determined
to be a likely valid transaction, e.g., with a relatively low probability of
being a fraudulent or
misused transaction.
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[00230] In some implementations, the second threshold is a configurable
parameter that is
indicative of a fuel likelihood score level below which a fuel transaction
event is likely to not be
a valid transaction, e.g., with a relatively high probability of being a
fraudulent or misused
transaction.
[00231] The first and second thresholds may be determined based on historical
fuel event data,
a type of vehicle, a load of the vehicle, a corresponding fuel mileage (fuel
efficiency) of the
vehicle, a type of terrain being driven, etc. The thresholds may be set by a
fleet operator and/or
by a payment instrument management system.
[00232] At a time of commencement of the trip (620), ti, in this particular
example, the fuel
likelihood score is calculated to be a number that is relatively low. This may
be based on
additional data that is indicative of a previous fueling event, and trip data
that includes a time and
distance of travel of the vehicle since the previous fueling event.
[00233] The fuel likelihood score is updated based on periodic recalculation
(630) of the fuel
likelihood score based on an elapsed time and/or an estimated distance of
travel, as well as be
updated based on received data from a user device (640), e.g., an
authenticated user device that
is associated with the vehicle. For example, in this illustrative example, the
calculation of fuel
likelihood scores at t2, t3, t3, and 16 may be based on periodic updates
(e.g., at a regular
predetermined interval), while the calculation at 16 and t7 are based on
received trip data from a
user device.
[00234] The periodic updates may be configured to have a particular
periodicity, which may
also change (decrease) with increasing elapsed time since the commencement of
the trip. The
periodicity of the updates may be also be adjusted based on received trip data
from a user device.
[00235] For example, the fuel likelihood score may be calculated at a first
calculation
periodicity at an initial portion of a trip and be calculated at a second
calculation periodicity (that
is smaller than the first calculation periodicity and thus more frequently)
towards a latter portion
of the trip.
[00236] The fuel likelihood score may also be calculated based on received
updates from the
user device that provide additional data on distance traversed by the vehicle
during the trip.
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[00237] In some implementations, a fuel likelihood score may be recalculated
every time that
an update is received from a user device associated with the trip. In some
implementations, a
minimum time may be specified for a fresh calculation of a fuel likelihood
score.
[00238] FIG. 6 additionally depicts a particular fuel likelihood score (655)
that is greater than
the first threshold, marking a fuel likelihood score that would automatically
cause a message to
be transmitted for a payment attribute to be adjusted, per techniques of this
disclosure. In this
illustrative example, the fuel likelihood score is updated (650) subsequent to
the fuel likelihood
score meeting the first threshold.
[00239] In this illustrative example, FIG. 6 also depicts a fuel likelihood
score (660) calculated
after the vehicle was refueled during the trip (at Ili) and reflects the fact
that it may be unlikely
that the vehicle will need to be refueled soon after. Subsequent updates of
the fuel likelihood
score (670) are also depicted, calculated based on received updates from the
user device.
[00240] FIG. 7 is a block diagram that illustrates example fuel likelihood
score calculation, in
accordance with some implementations.
[00241] The fuel likelihood score may be calculated using a variety of
techniques, e.g.,
deterministic techniques, Machine Learning (ML) based techniques, predictive
modeling
techniques, and combinations of techniques.
[00242] In this illustrative example, received trip data 715a is utilized at a
distance estimator
702 to determine a distance traveled since a previous fuel event. An estimated
distance traveled
may be determined based on trip data elements, e.g., location data, route
info, odometer data, etc.
[00243] The fuel consumption of the vehicle may be determined. An initial fuel
state is
obtained, either based on a record of an actual fuel event that is received or
estimated based on
historic trip and payment instrument data 708, e.g., from a fleet management
system, or based on
user entered data or an image.
[00244] Received image data may be processed by image processor 704, e.g., to
analyze an
image of an odometer or fuel gauge to extract mileage and fuel state data. A
fuel event predictor
706 may be utilized to predict a future fuel event, e.g., a predicted distance
and time to the next
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fuel event. The predictor may utilize Kalman filtering techniques, Wiener
filtering techniques,
etc. to predict a likely fuel event (time and distance). A fuel likelihood
score (A) 712 is calculated
based on the predicted fuel event based on trip data.
[00245] In some implementations, the fuel likelihood score (A) may be utilized
as the fuel
likelihood score to be utilized for comparison against a first and/or a second
threshold, e.g., first
and second thresholds described with reference to FIG. 4B, FIG. 5, or FIG. 6.
[00246] In some implementations, the fuel likelihood score (A) may be combined
at a score
generator 712 with a fuel likelihood score (B) 740 that is calculated based on
applying the trip
data to a machine learning model that has been previously trained on actual
route data and fueling
data from one or more fleets. A score generator 714 may be utilized to weight
the respective fuel
likelihood score streams to generate a fuel likelihood score 770 that may then
be utilized for
comparison against the first and/or second threshold.
[00247] The machine learning model can be implemented on a computer that
includes one or
more processors and memory with software instructions. In some
implementations, the one or
more processors may include one or more of a general-purpose central
processing unit (CPU), a
graphics processing unit (GPU), a machine-learning processor, an application-
specific integrated
circuit (ASIC), a field-programmable gate array (FPGA), or any other type of
processor.
[00248] In this illustrative example, supervised learning is used to train a
machine learning
(ML) model 730 based on training data 710 and a feedback generator 750. ML
model 730 may
be implemented using any suitable machine learning technique, e.g., a
clustering algorithm, a log
likelihood algorithm, a feedforward neural network (FNN), a convolutional
neural network
(CNN), etc. In some implementations, other machine learning techniques such as
Bayesian
models, support vector machines, hidden Markov models (HMMs), etc. can also be
used to
implement ML model 730.
[00249] The training data 710 includes trip data 715b for one or more trips
and corresponding
payment instrument data 725. The training data may be based on historical fuel
records, e.g., with
details of a time(s) that a vehicle was fueled, location(s) where a vehicle
was fueled, a price of
fuel, and a type of fuel. The training data can include data from one or more
fleets.
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[00250] In this illustrative example, trip data 715 are provided to a machine
learning (ML)
model under training 730. The ML model generates a predicted fuel likelihood
score 740 based
on a current state of the ML model and the trip data, e.g., data elements such
as distance traveled,
type of vehicle, driver data, etc. For example, the ML model may determine a
feature vector (or
embedding) based on features of trip data 715. The feature vector (or
embedding) may be a
mathematical, multi-dimensional representation generated based on trip data
715.
[00251] ML model 730 may generate a predicted fuel likelihood score for the
trip based on the
trip data, e.g., based on the feature vector, and/or based on similarity with
feature vectors of other
trip data associated with previous trips.
[00252] The predicted fuel likelihood score 740 generated by ML model 730 is
provided to
feedback generator 750.
[00253] Feedback generator 750 is also provided with the ground truth payment
instrument
data 725 corresponding to the trip, as measured and/or reported. Feedback 760
is generated by
feedback generator 750 based on a comparison of the predicted likelihood score
with the ground
truth payment instrument data, e.g., at what location was a vehicle refueled,
what distance was
covered, duration between fuel events, etc. For example, if predicted
likelihood score 740 is
within a predetermined threshold distance of ground truth payment instrument
data 725, positive
feedback may be provided as feedback 760, while if they are far apart and
outside a threshold
distance, negative feedback is provided to the ML model under training, which
may be updated
based on the received feedback using reinforcement learning techniques.
[00254] In some implementations, the MIL model includes one or more neural
networks. The
neural network(s) may be organized into a plurality of layers including a
plurality of layers. Each
layer may comprise a plurality of neural network nodes. Nodes in a particular
layer may be
connected to nodes in an immediately previous layer and nodes in an
immediately next layer. In
some implementations, the ML model may be a convolutional neural network
(CNN).
[00255] The training of the ML model may be performed periodically at
specified intervals, or
may be triggered by events. In some implementations, the training may be
repeated until a
threshold level of performance prediction accuracy is reached.
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[00256] FIG. 8 is a diagram of an example system architecture to provide high
availability real-
time authorization of transactions, in accordance with some implementations
[00257] The service consists of 2 independent groups of authentication control
pods running in
distributed computing systems, e.g., clusters that are located in distinct
regions. Each pod in a
group consists of the following containers:
[00258] = Processing Container:
[00259] o It performs the signaling with the ISO-8583 channel
[00260] o Distributes incoming authorization requests to workers (threads)
[00261] o It receives policy and state updates from the application scope and
stores them in
the database container.
[00262] = Database Container:
[00263] o It holds the policies associated with entities (cards, users, etc.).
[00264] o It holds all states necessary for the evaluation of policies.
[00265] o It performs 2-way synchronous replication to maintain up-to-date
state across the
authentication control pods in the same region. Real-time cross-regional
database replication is
not required as outlined below.
[00266] = Policy Agent:
[00267] o It receives a policy for evaluation.
[00268] o It receives the state information required for the evaluation of the
policy.
[00269] o It returns an approval or rejection policy evaluation result.
[00270] All the pods in a region are configured to connect to the same ISO-
8583 IP address.
Only a single pod in the group can establish a valid link to the ISO-8583
channel (active pod)
while the other pods remain in standby mode (standby pods). If the active pod
terminates, one of
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the standby pods will set up a valid link to the ISO-8583 channel. The pods in
a region are
distributed to distinct availability zones. According to this architecture, an
active pod will be
available in each region.
[00271] ISO-8583 Signaling
[00272] The card processor (payment network) delivers each authorization
request through all
active ISO-8583 channels. Thus, both regions receive the same set of
authorizations in the same
order. The processing containers in each region separately evaluate the
policies that apply to the
incoming authorization request and post their response to the ISO-8583
channel. The card
processor considers only the response that arrived first in the ISO-8583
channel and discards
further responses associated with the given authorization request.
[00273] High Availability
[00274] An RDBMS is attached to each Balance enquiry pod to enable low-latency
access and
allow each pod to remain self-contained without dependencies to external
services for the
approval of incoming authorization requests. The RDBMS is set up in multi-
master replication
mode. This is required to avoid a downtime during the fail-over of the master
to one of the slave
instances. An RDBMS cluster is set up per region.
[00275] Each pod receives application entity lifecycle events (organization,
user & card CRUD
operations) through a message/event streaming service, e.g., a Kafka topic to
which all pods are
subscribed. The pods in each region are part of the same consumer group and
therefore compete
for the same messages. When a pod pulls a message from the Kafka topic, it
updates its local
RDBMS and through replication, the change is reflected to the other pod in the
same region. Thus,
entity lifecycle events eventually reach all pods in all regions.
[00276] As mentioned in the ISO-8583 signaling section, each authorization is
delivered
through both ISO-8583 channels. The authorization in each region is received
by the main ISO-
8583 channel read/write thread and processed as follows:
[00277] 1. The authorization is committed to the local RDBMS with state set to
PENDING.
This is to ensure that the associated amount is reserved before the next
authorization is processed.
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[00278] 2. The authorization is assigned to a worker system for further
processing.
[00279] 3. The worker system evaluates the policies relevant to the
authorization.
[00280] 4. The worker system stores the result of the evaluation in the RDBMS
and returns
the result to the main ISO-8583 channel thread.
[00281] 5, The main thread writes the result to the ISO-8583 channel.
[00282] 6. The worker thread writes the authorization and the evaluation
result to a Pub/Sub
topic. The Pub/Sub service is a multi-regional low-latency reliable messaging
service. The
messages in this topic are used by pods in both regions to detect any
authorizations that may have
been missed and to synchronize their locally maintained list of
authorizations.
[00283] 7. The worker thread writes the authorization and the evaluation
result to GCS object
storage. This storage is used as backup storage for messages published to
Pub/Sub and allows
recovering these messages in case of a catastrophic failure in the region
hosting the Pub/Sub
messages of the corresponding pod. This is also a last-resort repository of
processed
authorizations in case of other failures that involve pods in both regions.
[00284] This layout is able to tolerate both zonal and regional failures
without suffering any
message loss.
[00285] Recovery
[00286] When a previously offline set of pods in a region is activated, it
first catches up with
the backlog of missed authorizations through Pub/Sub messaging. The active pod
in the
recovering region must observe messages coming both from Pub/Sub and the ISO-
8583 channel
until both are in-sync to ensure that it does not start evaluating new
authorizations from the ISO-
8583 channel until all previous authorizations have been received through
Pub/Sub.
[00287] The recovering pod performs the following:
[00288] 1. Pull messages from the Pub/Sub topic and store the RDBMS.
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[00289] 2. Pull messages from the ISO-8583 channel and store them to RDBMS
with status
PENDING.
[00290] 3. When a message that was received from Pub/Sub is already present in
the RDBMS
with status PENDING (i.e. received through the ISO-8583 channel), the
recovering pod is certain
that it has received and stored all messages in the RDBMS and that the current
balance is up-to-
date. The pod can then resume to normal operation where it evaluates new
messages from the
ISO-8583 channel and provides responses.
[00291] FIG. 9A is an example method for high availability real-time
authorization of
transactions at an authorization control pod, in accordance with some
implementations.
[00292] In some implementations, method 900 can be implemented, for example,
on processing
system 110described with reference to FIG. 1. In the described examples, the
implementing
system includes one or more digital processors or processing circuitry
("processors"), and one or
more storage devices. In some implementations, different components of one or
more servers
and/or clients can perform different blocks or other parts of the method 900.
In some examples,
a first device is described as performing blocks of method 900. Some
implementations can have
one or more blocks of method 900 performed by one or more other devices (e.g.,
other client
devices or server devices) that can send results or data to the first device.
[00293] In some implementations, the method 900, or portions of the method,
can be initiated
automatically by a system. In some implementations, the implementing system is
a first device.
For example, the method (or portions thereof) can be periodically performed,
or performed based
on one or more particular events or conditions, e.g., reception of a fresh
transaction, a signal
received from a card/payment processor, and/or one or more other conditions
occurring which
can be specified in settings read by the method.
[00294] Processing may begin at block 910.
[00295] At block 910, transaction information is received at one or more
authorization control
pods. Block 910 may be followed by block 915.
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[00296] At block 915, the received transaction information may be compared
against one or
more policies. Block 915 may be followed by block 920.
[00297] At block 920, it is determined whether the transaction information and
contextual data
meet the one or more policies. If it is determined at block 920 that the
transaction information and
contextual data meet (e.g., are in compliance with) the one or more policies,
block 920 may be
followed by block 925, else block 920 may be followed by block 930.
[00298] At block 925, the transaction is approved.
[00299] At block 930, the transaction is rejected.
[00300] FIG. 9B is an example method of high availability real-time
authorization of
transactions at a payment network (card processor), in accordance with some
implementations.
[00301] In some implementations, the method 950, or portions of the method,
can be initiated
automatically by a system. In some implementations, the implementing system is
a first device.
For example, the method (or portions thereof) can be periodically performed,
or performed based
on one or more particular events or conditions, e.g., notification of a
transaction from a merchant
terminal, and/or one or more other conditions occurring which can be specified
in settings read
by the method.
[00302] Processing may begin at block 960.
[00303] At block 960, transaction information is received at a payment network
(card processor)
from a merchant terminal or payment network. Block 960 may be followed by
block 965.
[00304] At block 965, the transaction information to two or more groups of
authorization pods.
Block 965 may be followed by block 970. At block 970, an approval or rejection
may be received
from the one or more groups of authorization pods. Block 970 may be followed
by block 975.
[00305] At block 975, it is determined whether the received approval or
rejection is the first
received for the transaction. If it is determined that the received approval
or rejection is the first
rejection, then the approval or rejection is processed at block 980, else
block 975 may be followed
by block 960.
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[00306] FIG. 10A illustrates an example of communication of state and policy
updates, in
accordance with some implementations.
[00307] The application scope communicates updates to the Authorization
Control pods
through messaging. Updates include state updates (e.g., addition of cards,
budget updates,
whitelisted/blacklisted MCCs, etc.) or policy updates (e.g., a MCC-based
approval policy, per-
diem spending policy, etc.).
[00308] Both Authorization Control pods receive policy & state updates and
they apply these
to their local databases and upload the new policies to the policy agent.
[00309] The active Authorization Control pod sends all incoming authorizations
to the
application scope through messaging. In case of authorization requests, the
result of the policy
evaluation is also sent.
[00310] In case of authorization rejection, in addition to the evaluation
result, the specific policy
that resulted in the rejection is included in the message to the application
scope.
[00311] Application Scope Data Model
[00312] Policies
[00313] Application scope policies are parameterized, and parameters are
configurable by the
customer. A set of policy templates are set up in the system. A policy
template consists of a
template of the policy script and a set of default values for the optional
template parameters.
Policy instances contain a set of policy template attributes and a reference
to the underlying policy
template. The combination of a policy template and a policy instance (i.e. the
set of instantiation
parameters) can be used to generate the target policy script.
[00314] A policy instance is associated with a single entity: a single card, a
single user or a
single organization/division. The associated entity UID, the entity type
(card, user, user-group)
and the generated policy script are sent to the Balance enquiry service
through a message queue.
[00315] Entity Updates
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[00316] The following operations are communicated to the Balance enquiry
service through a
message queue:
[00317] = Card creation, deletion and any other card update that affects the
card's operational
status (blocking, lost/stolen, etc.): The creation operation triggers the
creation of a Card entity in
the Balance enquiry service with an externalId = card.uid.
[00318] = User creation & deletion: The creation operation triggers the
creation of User entity
in the Balance enquiry service with an externalId = user.uid. In addition, a
UserGroup entity is
created in the Balance enquiry service with externalld = user.org.uid. A user
deletion operation
removes the user from the corresponding UserGroup.
[00319] Balance enquiry Data Model
[00320] Policies
[00321] Policies can be attached to entities. The entities can be individual
cards, individual users
or groups of entities. Policies can be added, updated or deleted. Policies are
enabled by default
and they cannot be disabled except when they expire or when they are deleted.
Policies may
optionally have an effective date and/or an expiration date denoting the
period during which the
policy will be considered. Policies are self-contained and their evaluation
does not produce any
side effects (e.g.,state changes). Multiple policies can be attached to users,
cards and groups.
Multiple policies can be attached to an entity. Since policies are self-
contained and they do not
produce side effects, multiple policies for an entity can be evaluated in
parallel.
[00322] FIG. 10B is a diagram that depicts an example domain model, in
accordance with some
implementations.
[00323] The monitored state depends exclusively on the input required by the
evaluation of the
policies.
[00324] In an example domain model, individual cards, users and groups of
cards and users can
be associated with one or more policies. Card grouping allows subsets of an
individual user to be
associated with a distinct set of policies. Similarly, organizations,
divisions and departments can
all be modeled as groups of users. In addition, any incoming authorization
request is stored in the
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database of each service pod and used to evaluate policies that require
historic data (e.g., budget,
per-diem, spend velocity).
[00325] A policy document contains the policy script and optionally an
effective date and an
expiration date. The policy script is generated at the application scope and
embeds small data that
does not change frequently (e.g., MCC ranges).
[00326] The following tables include the attribute name and description for
User Groups, User,
CardGroup, Card, and Policy.
Attribute Name Attribute Description
Group ID The external ID points to the application scope entity
UID (e.g., an
organization UID).
Group Qualifier This is used to disambiguate between different user grouping
approaches in
the application scope (organization, department, etc.) and map to a common
concept of user group in the Balance enquiry service.
Users Members of the group
Policies Policies attached to the group
[00327]
Attribute Name Attribute Description
User ID The external ID pointing to the application
scope user UID.
Cards List of cards owned by the user
Policies Policies attached to the user
[00328]
Attribute Name Attribute Description
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Group ID The external ID pointing to the application scope
entity UID. Currently
there's no corresponding application scope card grouping.
Group Qualifier Provisional attributes to distinguish between
different grouping entities
in the application scope and map to a common group in the Balance
enquiry service.
Cards Member cards of the group
Policies Policies attached to the group
[00329]
Attribute Name Attribute
Description
Card ID The external ID pointing to the application scope
card UID.
Surrogate ID Card processor-specific token associated with the
card. There's a one-
to-one mapping between the surrogate ID and the PAN and the card
processor provides a mapping function for converting PAN ¨>
Surrogate ID and Surrogate ID ¨> PAN.
Policies Policies attached to the card.
[00330]
Attribute Name Attribute
Description
Policy ID The external ID pointing to the application scope
policy instance UID.
This is included in the response to application scope upon the
approval or rejection of the authorization request.
Effective Date Consider policy only after effective date
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Expiration Date Consider policy only before expiration date
Content The policy script to evaluate
[00331] Authorizations
[00332] Received authorizations are immediately stored in the RDBMS with
status PENDING,
to reflect the available balance while the authorization is evaluated and
potentially subsequent
authorizations for the same account are being processed in parallel.
[00333] Expenses
[00334] When the settlement report is processed and authorizations are
reconciled with
settlements, a message is sent to Balance enquiry with an Expense object that
contains the final
settled amount, all associated authorizations details. The authorizations
associated with the
expense are marked reconciled and they are not considered in policy evaluation
criteria. Instead
the corresponding settled expense object is considered when evaluating
policies.
[00335] Refunds are handled as expenses with a negative amount value. Non-
business expenses
on a business card are flagged into the expense object and they are not
considered during policy
evaluation unless the policy dictates this explicitly. Business expenses on a
personal card are
added to Balance enquiry as expense objects without associated authorizations.
These (manual)
expenses may lack critical attributes that are present in auto expenses (e.g.,
MCC details).
[00336] Account Balance
[00337] Account balances are implemented as policies attached to UserGroup
entities. The
UserGroup contains the Users that have Cards backed by the corresponding bank
account. In the
unlikely scenario of Cards of a User backed by different bank accounts, a
corresponding
CardGroup per bank account needs to be maintained and the policy is attached
to the CardGroup.
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[00338] FIG. 11 depicts an example workflow for policy evaluation, in
accordance with some
implementations.
[00339] Policy Evaluation
[00340] The evaluation of a policy is performed by coupling the policy script
with input data.
The data is provided in the following ways:
= It can be embedded into the policy script. This is suitable for non-
dynamic data that have
a relatively small size.
= It can be provided as input at the time of evaluation. This is suitable
for dynamic data that
have to be provided explicitly on each occurrence of the policy evaluation.
= It can be pulled by the policy script during evaluation. This is suitable
for static or dynamic
data that have a large size.
[00341] In addition, the incoming authorization request is always provided as
input to the
policy. The authorization request is sanitized, that is, PCI-scope data is
either removed or
tokenized before the request is provided to the policy evaluation engine.
Specifically, all
occurrences of the PAN are converted to the corresponding surrogate ID.
[00342] The active pod that receives the authorization request must perform
the following steps:
[00343] 1. Retrieve all policies that apply directly or indirectly to the
incoming auth request.
These include:
[00344] 1. Policies directly associated with the card referenced in the auth
request.
[00345] 2. Policies associated with groups that the referenced card is a
member of.
[00346] 3. Policies associated with the cardholder (user) of the referenced
card.
[00347] 4. Policies associated with groups that the cardholder is a member of.
[00348] 2. Evaluate the retrieved policies providing the incoming auth request
as input to the
policies. Policies can be evaluated sequentially or in parallel. The process
terminates when one
of the policies rejects the auth request.
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[00349] 3. The pod sends the result of the evaluation through the ISO-8583
link. The pod
monitors steps 1 to 3. If the cumulative time for steps 1 to 3 exceeds a
predetermined threshold,
e.g., 4.5 seconds, the process is terminated and the pod returns a rejection
message to the ISO-
8583 link.
[00350] 4. The pod saves the auth request and the result of the evaluation to
the local RDBMS.
The data is replicated to the failover pod.
[00351] 5. The pod sends the auth request and the result of the evaluation to
the app scope
through a messaging/event log service, e.g., Kafka.
[00352] The result of the evaluation that is sent to the application scope
includes the following
attributes:
Attribute Name Attribute Description
Authorization The sanitized authorization request
Result The result of the evaluation. One of
approval
= decline
error
= timeout
Approved Policies In case of approval, the list of approved policies
will be non-empty.
Declined Policies In case of decline, the list of declined policies
will be non-empty. The
list of approved policies will be non-empty if more than one policies
have been evaluated. For example, if policies A, B, & C are evaluated
and A & B allowed the authorization but C rejected it. Then declined =
[C] and approved = [A,B]. Given that a decline leads to immediate
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termination of the evaluation, the declined policies set will have at most
a single element.
Error Code
In case of error, an error code and optional error message will be
included.
Error Message
[00353] FIG. 12 illustrates an example evaluation of a policy by a policy
engine, in accordance
with some implementations.
[00354] The policy script is uploaded to OPA through OPA' s management REST
API call.
Then the policy can be evaluated through the data REST API.
[00355] Besides the data that is embedded into the policy script, additional
data is provided
from the input at policy evaluation time and through HTTP invocations from
within the policy
script. HTTP invocations are made back into the Authorization Controller which
provides a local
(pod-internal) REST API to the OPA container in the pod. The API includes
calls related to
dynamic data that either have a large size or require non-trivial
aggregations. For example,. a
policy that enforces per-MCC budgets will initiate a call into the
Authorization Controller to
retrieve an up-to-date map of the per-MCC spending for the period specified in
the policy.
[00356] A sample policy script is utilized to call a REST endpoint to retrieve
the current per-
MCC spending which is then compared against the per-MCC budgets.
[00357] FIG. 13 is a block diagram of an example computing device 1300 which
may be used
to implement any of the features described herein. In one example, device 1300
may be used to
implement a computer device (e.g., 110, 120, 130, 140, and/or 150 of FIG. 1),
and perform
appropriate method implementations described herein. Computing device 1300 can
be any
suitable computer system, server, or other electronic or hardware device. For
example, the
computing device 1300 can be an instance in a cloud computing or other
distributed computing
system, a mainframe computer, desktop computer, workstation, portable
computer, or electronic
device (portable device, mobile device, cell phone, smartphone, tablet
computer, television, TV
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set top box, personal digital assistant (PDA), media player, game device,
wearable device, etc.).
In some implementations, device 1300 includes a processor 1302, a memory 1304,
input/output
(I/O) interface 1306, and audio/video input/output devices 1314.
[00358] Processor 1302 can be one or more processors and/or processing
circuits to execute
program code and control basic operations of the device 1300. A "processor"
includes any
suitable hardware and/or software system, mechanism or component that
processes data, signals
or other information. A processor may include a system with a general-purpose
central processing
unit (CPU), multiple processing units, dedicated circuitry for achieving
functionality, or other
systems. Processing need not be limited to a particular geographic location,
or have temporal
limitations. For example, a processor may perform its functions in "real-
time," "offline," in a
"batch mode," etc. Portions of processing may be performed at different times
and at different
locations, by different (or the same) processing systems. A computer may be
any processor in
communication with a memory.
[00359] Computer readable medium (memory) 1306 is typically provided in device
1300 for
access by the processor 1302, and may be any suitable processor-readable
storage medium, e.g.,
random access memory (RAM), read-only memory (ROM), Electrical Erasable Read-
only
Memory (EEPROM), Flash memory, etc., suitable for storing instructions for
execution by the
processor, and located separate from processor 1302 and/or integrated
therewith. Memory 1304
can store software operating on the server device 1300 by the processor 1302,
including an
operating system 1304, one or more applications 1310 and application data
1312. In some
implementations, application 1310 can include instructions that enable
processor 1302 to perform
the functions (or control the functions of) described herein, e.g., some or
all of the methods
described with respect to FIG. 4A, FIG. 4B, FIG. 5, FIG. 9A, or FIG. 9B.
[00360] Elements of software in memory 1306 can alternatively be stored on any
other suitable
storage location or computer-readable medium. In addition, memory 1306 (and/or
other
connected storage device(s)) can store instructions and data used in the
features described herein.
Memory 1306 and any other type of storage (magnetic disk, optical disk,
magnetic tape, or other
tangible media) can be considered "storage" or "storage devices."
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[00361] An I/O interface can provide functions to enable interfacing the
server device 1300
with other systems and devices. For example, network communication devices,
storage devices
(e.g., memory and/or data stores), and input/output devices can communicate
via the interface. In
some implementations, the I/O interface can connect to interface devices
including input devices
(keyboard, pointing device, touchscreen, microphone, camera, scanner, etc.)
and/or output
devices (display device, speaker devices, printer, motor, etc.).
[00362] The audio/video input/output devices can include a user input device
(e.g., a mouse,
etc.) that can be used to receive user input, a display device (e.g., screen,
monitor, etc.) and/or a
combined input and display device, that can be used to provide graphical
and/or visual output.
[00363] For ease of illustration, FIG. 13 shows one block for each of
processor 1302, memory
1306. These blocks may represent one or more processors or processing
circuitries, operating
systems, memories, I/O interfaces, applications, and/or software engines. In
other
implementations, device 1300 may not have all of the components shown and/or
may have other
elements including other types of elements instead of, or in addition to,
those shown herein. While
the processing system is described as performing operations as described in
some
implementations herein, any suitable component or combination of components of
processing
systems, or any suitable processor or processors associated with such a
system, may perform the
operations described.
[00364] A user device can also implement and/or be used with features
described herein.
Example user devices can be computer devices including some similar components
as the device
1300, e.g., processor(s) 1302, memory 1306, etc. An operating system, software
and applications
suitable for the client device can be provided in memory and used by the
processor. The I/O
interface for a client device can be connected to network communication
devices, as well as to
input and output devices, e.g., a microphone for capturing sound, a camera for
capturing images
or video, a mouse for capturing user input, a gesture device for recognizing a
user gesture, a
touchscreen to detect user input, audio speaker devices for outputting sound,
a display device for
outputting images or video, or other output devices. A display device within
the audio/video
input/output devices, for example, can be connected to (or included in) the
device 1300 to display
images pre- and post-processing as described herein, where such display device
can include any
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suitable display device, e.g., an LCD, LED, or plasma display screen, CRT,
television, monitor,
touchscreen, 3-D display screen, projector, or other visual display device.
Some implementations
can provide an audio output device, e.g., voice output or synthesis that
speaks text.
[00365] One or more methods described herein (e.g., methods 400, 450, 500,
900, and/or 950)
can be implemented by computer program instructions or code, which can be
executed on a
computer. For example, the code can be implemented by one or more digital
processors (e.g.,
microprocessors or other processing circuitry), and can be stored on a
computer program product
including a non-transitory computer readable medium (e.g., storage medium),
e.g., a magnetic,
optical, electromagnetic, or semiconductor storage medium, including
semiconductor or solid
state memory, magnetic tape, a removable computer diskette, a random access
memory (RAM),
a read-only memory (ROM), flash memory, a rigid magnetic disk, an optical
disk, a solid-state
memory drive, etc. The program instructions can also be contained in, and
provided as, an
electronic signal, for example in the form of software as a service (SaaS)
delivered from a server
(e.g., a distributed system and/or a cloud computing system). Alternatively,
one or more methods
can be implemented in hardware (logic gates, etc.), or in a combination of
hardware and software.
Example hardware can be programmable processors (e.g., Field-Programmable Gate
Array
(FPGA), Complex Programmable Logic Device), general purpose processors,
graphics
processors, Application Specific Integrated Circuits (ASICs), and the like.
One or more methods
can be performed as part of or component of an application running on the
system, or as an
application or software running in conjunction with other applications and
operating systems.
[00366] One or more methods described herein can be run in a standalone
program that can be
run on any type of computing device, a program run on a web browser, a mobile
application
("app") run on a mobile computing device (e.g., cell phone, smart phone,
tablet computer,
wearable device (wristwatch, armband, jewelry, headwear, goggles, glasses,
etc.), laptop
computer, etc.). In one example, a client/server architecture can be used,
e.g., a mobile computing
device (as a client device) sends user input data to a server device and
receives from the server
the final output data for output (e.g., for display). In another example, all
computations can be
performed within the mobile app (and/or other apps) on the mobile computing
device. In another
example, computations can be split between the mobile computing device and one
or more server
devices.
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[00367] Although the description has been described with respect to particular
implementations thereof, these particular implementations are merely
illustrative. Concepts
illustrated in the examples may be applied to other examples and
implementations.
[00368] The functional blocks, operations, features, methods, devices, and
systems described
in the present disclosure may be integrated or divided into different
combinations of systems,
devices, and functional blocks as would be known to those skilled in the art.
Any suitable
programming language and programming techniques may be used to implement the
routines of
particular implementations. Different programming techniques may be employed,
e.g.,
procedural or object-oriented. The routines may execute on a single processing
device or multiple
processors. Although the steps, operations, or computations may be presented
in a specific order,
the order may be changed in different particular implementations. In some
implementations,
multiple steps or operations shown as sequential in this specification may be
performed at the
same time. The term "automatic" and variations thereof, as used herein, refers
to any process or
operation, which is typically continuous or semi-continuous, done without
material human input
when the process or operation is performed. However, a process or operation
can be automatic,
even though performance of the process or operation uses material or
immaterial human input, if
the input is received before performance of the process or operation. Human
input is deemed to
be material if such input influences how the process or operation will be
performed. Human input
that consents to the performance of the process or operation is not deemed to
be "material."
[00369] The term "computer-readable medium" as used herein refers to any
computer-readable
storage and/or transmission medium that participates in providing instructions
to a processor for
execution. Such a computer-readable medium can be tangible, non-transitory,
and non-transient
and take many forms, including but not limited to, non-volatile media,
volatile media, and
transmission media and includes without limitation random access memory
("RAM"), read only
memory ("ROM"), and the like. Non-volatile media includes, for example, NVRAM,
or magnetic
or optical disks. Volatile media includes dynamic memory, such as main memory.
Common forms
of computer-readable media include, for example, a floppy disk (including
without limitation a
Bernoulli cartridge, ZIP drive, and JAZ drive), a flexible disk, hard disk,
magnetic tape or
cassettes, or any other magnetic medium, magneto-optical medium, a digital
video disk (such as
CD-ROM), any other optical medium, punch cards, paper tape, any other physical
medium with
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patterns of holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, a solid state
medium like
a memory card, any other memory chip or cartridge, a carrier wave as described
hereinafter, or
any other medium from which a computer can read. A digital file attachment to
e-mail or other
self-contained information archive or set of archives is considered a
distribution medium
equivalent to a tangible storage medium. When the computer-readable media is
configured as a
database, it is to be understood that the database may be any type of
database, such as relational,
hierarchical, object-oriented, and/or the like. Accordingly, the disclosure is
considered to include
a tangible storage medium or distribution medium and prior art-recognized
equivalents and
successor media, in which the software implementations of the present
disclosure are stored.
Computer-readable storage medium commonly excludes transient storage media,
particularly
electrical, magnetic, electromagnetic, optical, magneto-optical signals.
[00370] A "computer readable storage medium" may be, for example, but not
limited to, an
electronic, magnetic, optical, electromagnetic, infrared, or semiconductor
system, apparatus, or
device, or any suitable combination of the foregoing. More specific examples
(a non-exhaustive
list) of the computer readable storage medium would include the following: an
electrical
connection having one or more wires, a portable computer diskette, a hard
disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable read-only
memory
(EPROM or Flash memory), an optical fiber, a portable compact disc read-only
memory (CD-
ROM), an optical storage device, a magnetic storage device, or any suitable
combination of the
foregoing. In the context of this document, a computer readable storage medium
may be any
tangible medium that can contain or store a program for use by or in
connection with an instruction
execution system, apparatus, or device.
[00371] A "computer readable signal medium" may be any computer readable
medium that is
not a computer readable storage medium and that can communicate, propagate, or
transport a
program for use by or in connection with an instruction execution system,
apparatus, or device.
A computer readable signal medium may convey a propagated data signal with
computer readable
program code embodied therein, for example, in baseband or as part of a
carrier wave. Such a
propagated signal may take any of a variety of forms, including, but not
limited to, electro-
magnetic, optical, or any suitable combination thereof Program code embodied
on a computer
readable signal medium may be transmitted using any appropriate medium,
including but not
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limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable
combination of the
foregoing.
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